The intent of data storytelling is to gain a desired response from your audience, but not at any cost. Data storytelling is not about shaping data to fit a predetermined narrative. The story must emerge organically from the data itself. Ethical data storytelling involves using visualizations along with narrative to communicate insights in a way that allows stakeholders to independently understand key takeaways, make informed decisions, and take meaningful action.
Ethics is the branch of philosophy that deals with issues of right and wrong in human affairs. Questions of ethics come into play whenever you engage in data storytelling. Ethical issues are present at every stage of the data storytelling process from business question formation to analyzing the data to the organizational context in which it is presented.
When approached ethically, data storytelling serves to clarify complex information and drive meaningful conversations. A commitment to ethical communication ensures that decisions are guided by facts rather than personal biases or misleading interpretations.
Uncovering Meaning Without Manipulation
2000 years ago the Roman rhetorician Quintilian, when reflecting on the inherent power of oratory, noted that the ideal of speechmaking is “the good person speaking well”. All forms of communication, be they digital, visual, written, verbal or nonverbal, have the power to move, influence, motivate, incite and or inspire an audience to act. The ethical communicator takes into account intent, means, and context in the development of a data story.
Just as Quintilian emphasized that rhetoric should serve both skill and virtue, ethical data storytelling requires both analytical savvy and integrity. The principled data-driven professional uncovers meaning without manipulation. The most effective data stories are not just well-constructed, they are truthful, transparent, and responsible, ensuring that insights guide decisions in an ethical and meaningful way.
Effective, ethical data stories begin with a clear goal and purpose as well as a knowledge of and goodwill toward the intended audience. They begin with data communication planning that is sound in its intent and analysis that is free of biases.
Data communication planning is the pre-production time of crafting your data message. To truly realize the benefits of data-driven decision making means starting with the end in mind. It involves being clear on what you hope to achieve, knowing your audience and having their goodwill in mind as well as organizing your content in a way that connects with them.
Data communication planning is audience centered. Audience-centeredness keeps the audience foremost in mind at every step of the data visualization preparation and presentation. Having the goodwill toward the audience begins with a clear understanding of stakeholder needs and business objectives. The best audience analysis tools for your data storytelling are those that get at the heart of their needs and desires.
Asking the Right Questions
The first step in ethical data storytelling is defining the right questions based on the intended outcome. These questions should be designed to uncover meaningful insights rather than confirm preexisting assumptions.
Aligning your measurement efforts with your business objectives drawn from organizational goals will focus your data activities on the key insights that are the current priority. Prioritization of strategic initiatives will help you clarify the questions to ask and the data to collect.
Use your alignment of organizational KPI’s coupled with your intellectual curiosity to learn something new, figure something out, or to make a discovery. Exhibiting intellectual curiosity means admitting that you don’t have all the answers. Organizations with a healthy data culture are ones who encourage this by allowing their decision makers to grow and develop by mining the data for answers, even if it’s those answers end up being unexpected or welcome.
Collecting Data with Integrity
Data should be gathered in a manner that directly answers business questions, even if the results are unexpected or unfavorable.
Creating a data-driven culture is about replacing gut feeling with decisions based on data. The main aim is to empower all employees to actively use data to enhance their daily work and to reach their potential by making decisions easier and more strategic, whether working individually or facilitating a group decision making process.
A healthy data culture means that everyone recognizes that they are either a creator or consumer of the data. The safety and quality of the data is a shared responsibility. Assuring the methodologies used to collect the data are reliable and valid is an ethical responsibility of a data storyteller. Ethical data storytelling means acknowledging gaps and uncertainties rather than forcing conclusions.
Recognizing and Managing Bias
A data-driven mindset also requires an awareness of inherent biases in the data collection and analysis. Ethical data storytellers have an awareness of how bias can be introduced through question selection, data collection and analysis, and the methods used to communicate insights.
A strong understanding of bias helps ensure that insights remain accurate and actionable rather than misleading or skewed. Unchecked biases have the ability to influence data interpretation in subtle but significant ways. Bias in data storytelling can occur when subjective preferences or beliefs distort findings, leading to incorrect conclusions. These influence may be individual or organizational in nature. They are important to be aware of so that they can be effectively recognized and managed.
Ethical Data Storytelling: Tips for Working with Data Bias
Being aware of common bias pitfalls and how to avoid them is essential to maintaining ethical standards in data storytelling. Here are some tips to avoid common biases in working with data.
Confirmation Bias – The tendency to seek out, interpret, or recall information in a way that supports preexisting beliefs. For example, you have a program that assists patients and you go into examining the relationship between the program and patient experience scores with the express purpose of showing that the program is having a strong positive influence.
✅ How to Avoid It:
Approach data with curiosity and open mindset and allow conclusions to emerge organically rather than looking for evidence to support a predetermined narrative.
Feedback Effect Bias – The tendency to prioritize external feedback over data-driven insights. This occurs when analysts give more weight to a particular stakeholder's opinions or anecdotal feedback than to statistical evidence. For example, a vocal volunteer group within a large umbrella program frequently requests additional staff resources. To appease them, leadership consistently allocates more support. However, an analysis of the data shows that this group already receives twice the resources and recognition compared to other volunteer programs. Despite clear evidence of imbalance, decisions continue to be driven by the loudest voices rather than objective data.
✅ How to Avoid It:
False Cause Fallacy – Assuming causation between two correlated variables without sufficient evidence. Just because two data points move together does not mean one causes the other. Causation usually requires a more complex inferential data analysis, whereas most decision makers are using descriptive data in decision making.
✅ How to Avoid It:
Recency Bias – The tendency to give undue weight to recent events while overlooking historical data. For example, a nonprofit sees several months of increased giving following a high-profile community event and assumes donor engagement is improving. Leadership shifts focus to replicating the event’s success, redirecting fundraising efforts toward similar initiatives. However, a deeper look at historical data reveals that the spike was tied to temporary media attention, while year-over-year donations have been steadily declining. By focusing too much on recent gains, the organization risks misallocating resources and missing the bigger issue of donor attrition.
✅ How to Avoid It:
Ethical Storytelling with Data
Beyond planning, analysis, and the organizational culture, ethical considerations must also be applied to the development of the data story, including the visualizations. A poorly designed chart can mislead an audience just as much as flawed analysis.
According to the Business Analysis Body of Knowledge (BABOK), ethical behavior in business analysis involves considering the impact of findings on all stakeholders and ensuring that solutions are fair and transparent. (BABOK 9.2.1). Ethics is an underlying core competency of a business analyst that allows them to gain the trust and respect of stakeholders. It demonstrates a commitment to the goodwill of the audience.
In this context, transparent data storytelling requires:
Clear and Honest Communication – Stakeholders should be presented with findings in a transparent way, avoiding manipulations that make results seem more favorable than they are.
Full Disclosure –Any potential conflicts of interest, data limitations, or areas of uncertainty should be communicated openly to maintain trust and credibility.
Stakeholder Awareness – Ethical decision-making ensures that all affected groups understand the rationale behind conclusions, even when decisions may not align with their interests.
While effective communication should be directed toward creating line of sight for your stakeholders, that does not mean altering the data. The iterative process doesn’t mean having a different story to tell for each audience, it means tailoring the same message to different audiences but adapting to resonate with their needs.
Ethical Data Storytelling: Tips for Working with Visualizations
Since visualizations form the backbone of data storytelling, ensuring their integrity is essential. Misleading visual representations can distort understanding, leading to flawed decisions. Here are some key ethical pitfalls to avoid when designing data visualizations, along with strategies to prevent these errors.
Manipulating the Y-Axis Baseline
One unethical chart design issue is raised by adjusting the y-axis baseline to exaggerate or downplay differences in data. For example, a bar chart that starts at a value other than zero can make small changes appear more significant than they actually are, misleading stakeholders into drawing incorrect conclusions.
✅ How to Avoid It:
Always check whether your visualization’s baseline starts at zero. If a nonzero baseline is necessary (e.g., in line charts to highlight small fluctuations), clearly label the axis and provide context to prevent misinterpretation.
Misuse of Mean, Median, and Mode
Statistical measures like mean, median, and mode can tell very different stories about the same dataset. Averages (mean) can be skewed by extreme values, while the median provides a better representation of central tendency in a skewed distribution. The mode, which represents the most frequently occurring value, might not be meaningful in some contexts.
✅ How to Avoid It:
Misrepresenting Sample Size and Population
A small or unrepresentative sample can produce misleading insights that do not generalize to the broader population. Charts and statistics based on limited data may lead to incorrect conclusions if the audience assumes they represent a larger group.
✅ How to Avoid It:
Selective Data Omission
Excluding specific time periods, data points, or unfavorable results to influence interpretation is another ethical concern. This practice can present a distorted view of trends and mislead stakeholders into thinking a pattern is more favorable (or unfavorable) than it truly is.
✅ How to Avoid It:
You can see these errors in play along with corresponding commentary by checking out a great real-world visualization example taken from Journalism.org as shared by Perceptual Edge.
Summary
Ethical data storytelling is about more than just presenting numbers—it is about ensuring that those numbers are used to drive informed and responsible decision-making. The effectiveness of ethical data storytelling can be measured by how well concerns are identified and resolved, whether stakeholders find decisions transparent and fair, and whether reasoning is clearly articulated and understood.
By prioritizing integrity, recognizing biases, and upholding transparency in analysis and visualization, data professionals can create meaningful stories that guide organizations toward ethical, data-driven strategies.
Ethical data storytelling relies on visual honesty. Every chart, graph, and statistic should aim to clarify, not obscure. Whether analyzing trends, forecasting outcomes, or influencing policy, maintaining ethical standards in data storytelling is essential to building trust, credibility, and long-term success.
Roseanna Galindo is Principal at Periscope Business Process Analysis, specializing in organizational learning and development. She is dedicated to advancing data literacy, enhancing healthcare experiences, and empowering volunteer leaders. Explore Roseanna’s expertise and insights on her blog, The Periscope Insighter, starting with the opening post, "Venn the Time is Right."
Roseanna offers a range of professional development services, including training workshops, keynote speaking, and executive coaching.
Visit PeriscopeBPA.com for more information or click on the button below to schedule a time to talk
If you have found this article insightful, please share on social to help other like-minded business leaders to find their way here.
Comment. Share. Like. Subscribe. Thank you!
Comments